Analysis of groundwater flow in arid areas with limited hydrogeological data using the Grey Model: a case study of the Nubian Sandstone, Kharga Oasis, Egypt

Mahmod, Wael Elham; Watanabe, Kunio; Zahr-Eldeen, Ashraf A.

Analysis of groundwater flow in arid areas with limited hydrogeological data using the Grey Model: a case study of the Nubian Sandstone, Kharga Oasis, Egypt | Analyse du débit de nappe souterraine dans une zone aride avec des données hydrogéologiques limitées utilisant le Modèle de Grey: étude de cas du Grès Nubien, oasis de Kharga, Egypte Análisis de flujo de agua subterránea en áreas áridas con datos hidrogeológicos limitados usando el modelo Grey: un caso de estudio de las Areniscas de Nubian, Oasis de Kharga, Egipto Análise do fluxo das águas subterrâneas em zonas áridas com dados hidrogeológicos limitados usando o Modelo Cinza: um estudo de caso do Arenito Núbio, Oásis Kharga, Egito

2013

Mahmod, Wael Elham | Watanabe, Kunio | Zahr-Eldeen, Ashraf A.

Management of groundwater resources can be enhanced by using numerical models to improve development strategies. However, the lack of basic data often limits the implementation of these models. The Kharga Oasis in the western desert of Egypt is an arid area that mainly depends on groundwater from the Nubian Sandstone Aquifer System (NSAS), for which the hydrogeological data needed for groundwater simulation are lacking, thereby introducing a problem for model calibration and validation. The Grey Model (GM) was adopted to analyze groundwater flow. This model combines a finite element method (FEM) with a linear regression model to try to obtain the best-fit piezometric-level trends compared to observations. The GM simulation results clearly show that the future water table in the northeastern part of the study area will face a severe drawdown compared with that in the southwestern part and that the hydraulic head difference between these parts will reach 140 m by 2060. Given the uncertainty and limitation of available data, the GM produced more realistic results compared with those obtained from a FEM alone. The GM could be applied to other cases with similar data limitations.

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